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Intelligent Reasoning Lab [Institute of Scientific and Industrial Research]
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A lab studying causal discovery, which explores causal relationships from data.
AboutAbout the lab
Making causal discovery a standard analysis method
The Intelligent Reasoning Lab is a data science and AI lab that focuses on causal discovery.
The lab aims to establish causal discovery as a standard analysis method in scientific research and in industry and policy settings, laying the foundation for a society where causal inference is used routinely.
Working with researchers and practitioners in fields ranging from the natural and social sciences to engineering and medicine, the lab studies mathematical methodologies for uncovering causal relationships.
The lab develops new methodologies that can estimate causal relationships from observational data, even in situations where interventional experiments are difficult.
Key terms
- Causal discovery
- Estimating causal relationships from data, that is, which variables are the causes of which other variables
- Observational data
- Data collected as it naturally occurs, without people manipulating the conditions
- Interventional experiment
- An experiment in which people change a condition thought to be a cause and examine the difference in outcomes
ResearchResearch themes
The lab studies mathematical methodologies for uncovering causal relationships, centered on causal discovery. It works with researchers and practitioners in various fields, aiming to establish causal discovery as a standard analysis method.
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MembersFaculty
Members of the Intelligent Reasoning Lab include faculty, researchers, visiting faculty and researchers, students in the master's program and undergraduate students, administrative assistants, and technical assistants.
- ProfessorShohei Shimizu
- Associate ProfessorPHAM Thong
- Assistant ProfessorHiroshi Takeuchi
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Bayesian network gene” Lorenahoxhallari / CC BY-SA 4.0 (cropped)
- Methods for estimating causal relationships from observational data: “Dominoes falling” Kurt:S / CC BY 2.0 (cropped)
- Related lab (Terahertz Science Lab [Institute of Laser Engineering]): “ESA laser testing ESA372639” European Space Agency / CC BY-SA 3.0 igo (cropped)
- Related lab (Knowledge Science Lab [Institute of Scientific and Industrial Research]): “Subvocal speech recognition” Dominic Hart, NASA / Public domain (cropped)
- Related lab (Photon and Quantum Devices Lab [Institute of Laser Engineering]): “Extreme ultraviolet (EUV) frequency comb ( )” National Institute of Standards and Technology / Public domain (cropped)
- Related lab (Electron Microscopy Theory Lab [Research Center for Ultrahigh Voltage Electron Microscopy]): “JEOL JEM-200A transmission electron microscope -08” High Contrast / CC BY 3.0 de (cropped)
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